close

Showing 13 of total 13 results (show query)

philchalmers

mirt:Multidimensional Item Response Theory

Analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported, as well as a wide family of probabilistic unfolding models.

Maintained by Phil Chalmers. Last updated 10 days ago.

irtmirtopenblascppopenmp

94.2 match 210 stars 14.98 score 2.5k scripts 40 dependents

alexanderrobitzsch

sirt:Supplementary Item Response Theory Models

Supplementary functions for item response models aiming to complement existing R packages. The functionality includes among others multidimensional compensatory and noncompensatory IRT models (Reckase, 2009, <doi:10.1007/978-0-387-89976-3>), MCMC for hierarchical IRT models and testlet models (Fox, 2010, <doi:10.1007/978-1-4419-0742-4>), NOHARM (McDonald, 1982, <doi:10.1177/014662168200600402>), Rasch copula model (Braeken, 2011, <doi:10.1007/s11336-010-9190-4>; Schroeders, Robitzsch & Schipolowski, 2014, <doi:10.1111/jedm.12054>), faceted and hierarchical rater models (DeCarlo, Kim & Johnson, 2011, <doi:10.1111/j.1745-3984.2011.00143.x>), ordinal IRT model (ISOP; Scheiblechner, 1995, <doi:10.1007/BF02301417>), DETECT statistic (Stout, Habing, Douglas & Kim, 1996, <doi:10.1177/014662169602000403>), local structural equation modeling (LSEM; Hildebrandt, Luedtke, Robitzsch, Sommer & Wilhelm, 2016, <doi:10.1080/00273171.2016.1142856>).

Maintained by Alexander Robitzsch. Last updated 3 months ago.

item-response-theoryopenblascpp

17.2 match 23 stars 10.01 score 280 scripts 22 dependents

mmansolf

AlignLV:Multiple Group Item Response Theory Alignment Helpers for 'lavaan' and 'mirt'

Allows for multiple group item response theory alignment a la 'Mplus' to be applied to lists of single-group models estimated in 'lavaan' or 'mirt'. Allows item sets that are overlapping but not identical, facilitating alignment in secondary data analysis where not all items may be shared across assessments.

Maintained by Maxwell Mansolf. Last updated 5 months ago.

16.5 match 3.00 score 2 scripts

herulor

DFIT:Differential Functioning of Items and Tests

A set of functions to perform Raju, van der Linden and Fleer's (1995, <doi:10.1177/014662169501900405>) Differential Functioning of Items and Tests (DFIT) analyses. It includes functions to use the Monte Carlo Item Parameter Replication approach (Oshima, Raju, & Nanda, 2006, <doi:10.1111/j.1745-3984.2006.00001.x>) for obtaining the associated statistical significance tests cut-off points. They may also be used for a priori and post-hoc power calculations (Cervantes, 2017, <doi:10.18637/jss.v076.i05>).

Maintained by Victor H. Cervantes. Last updated 9 months ago.

11.3 match 2.30 score 20 scripts

philchalmers

mirtCAT:Computerized Adaptive Testing with Multidimensional Item Response Theory

Provides tools to generate HTML interfaces for adaptive and non-adaptive tests using the shiny package (Chalmers (2016) <doi:10.18637/jss.v071.i05>). Suitable for applying unidimensional and multidimensional computerized adaptive tests (CAT) using item response theory methodology and for creating simple questionnaires forms to collect response data directly in R. Additionally, optimal test designs (e.g., "shadow testing") are supported for tests that contain a large number of item selection constraints. Finally, package contains tools useful for performing Monte Carlo simulations for studying test item banks.

Maintained by Phil Chalmers. Last updated 5 months ago.

catirtopenblascpp

1.8 match 95 stars 9.41 score 62 scripts 3 dependents

bocaccio

plink:IRT Separate Calibration Linking Methods

Item response theory based methods are used to compute linking constants and conduct chain linking of unidimensional or multidimensional tests for multiple groups under a common item design. The unidimensional methods include the Mean/Mean, Mean/Sigma, Haebara, and Stocking-Lord methods for dichotomous (1PL, 2PL and 3PL) and/or polytomous (graded response, partial credit/generalized partial credit, nominal, and multiple-choice model) items. The multidimensional methods include the least squares method and extensions of the Haebara and Stocking-Lord method using single or multiple dilation parameters for multidimensional extensions of all the unidimensional dichotomous and polytomous item response models. The package also includes functions for importing item and/or ability parameters from common IRT software, conducting IRT true score and observed score equating, and plotting item response curves/surfaces, vector plots, information plots, and comparison plots for examining parameter drift.

Maintained by Jonathan P. Weeks. Last updated 8 years ago.

3.6 match 2 stars 4.58 score 44 scripts 1 dependents

bfast2

bfast:Breaks for Additive Season and Trend

Decomposition of time series into trend, seasonal, and remainder components with methods for detecting and characterizing abrupt changes within the trend and seasonal components. 'BFAST' can be used to analyze different types of satellite image time series and can be applied to other disciplines dealing with seasonal or non-seasonal time series, such as hydrology, climatology, and econometrics. The algorithm can be extended to label detected changes with information on the parameters of the fitted piecewise linear models. 'BFAST' monitoring functionality is described in Verbesselt et al. (2010) <doi:10.1016/j.rse.2009.08.014>. 'BFAST monitor' provides functionality to detect disturbance in near real-time based on 'BFAST'- type models, and is described in Verbesselt et al. (2012) <doi:10.1016/j.rse.2012.02.022>. 'BFAST Lite' approach is a flexible approach that handles missing data without interpolation, and will be described in an upcoming paper. Furthermore, different models can now be used to fit the time series data and detect structural changes (breaks).

Maintained by Dainius Masiliūnas. Last updated 5 months ago.

cpp

1.5 match 42 stars 7.98 score 212 scripts 1 dependents

micbtz

equateIRT:IRT Equating Methods

Computation of direct, chain and average (bisector) equating coefficients with standard errors using Item Response Theory (IRT) methods for dichotomous items (Battauz (2013) <doi:10.1007/s11336-012-9316-y>, Battauz (2015) <doi:10.18637/jss.v068.i07>). Test scoring can be performed by true score equating and observed score equating methods. DIF detection can be performed using a Wald-type test (Battauz (2019) <doi:10.1007/s10260-018-00442-w>). The package includes tests to assess the stability of the equating transformations (Battauz(2022) <doi:10.1111/stan.12277>).

Maintained by Michela Battauz. Last updated 5 months ago.

2.3 match 4.70 score 35 scripts 2 dependents

bfast2

strucchangeRcpp:Testing, Monitoring, and Dating Structural Changes: C++ Version

A fast implementation with additional experimental features for testing, monitoring and dating structural changes in (linear) regression models. 'strucchangeRcpp' features tests/methods from the generalized fluctuation test framework as well as from the F test (Chow test) framework. This includes methods to fit, plot and test fluctuation processes (e.g. cumulative/moving sum, recursive/moving estimates) and F statistics, respectively. These methods are described in Zeileis et al. (2002) <doi:10.18637/jss.v007.i02>. Finally, the breakpoints in regression models with structural changes can be estimated together with confidence intervals, and their magnitude as well as the model fit can be evaluated using a variety of statistical measures.

Maintained by Dainius Masiliunas. Last updated 5 months ago.

openblascpp

1.5 match 5 stars 5.18 score 4 scripts 2 dependents

ddebeer

scDIFtest:Item-Wise Score-Based DIF Detection

Detection of item-wise Differential Item Functioning (DIF) in fitted 'mirt', 'multipleGroup' or 'bfactor' models using score-based structural change tests. Under the hood the sctest() function from the 'strucchange' package is used.

Maintained by Dries Debeer. Last updated 5 years ago.

1.6 match 4.18 score 9 scripts 1 dependents

hwangq

irtQ:Unidimensional Item Response Theory Modeling

Fit unidimensional item response theory (IRT) models to a mixture of dichotomous and polytomous data, calibrate online item parameters (i.e., pretest and operational items), estimate examinees' abilities, and examine the IRT model-data fit on item-level in different ways as well as provide useful functions related to IRT analyses such as IRT model-data fit evaluation and differential item functioning analysis. The bring.flexmirt() and write.flexmirt() functions were written by modifying the read.flexmirt() function (Pritikin & Falk (2022) <doi:10.1177/0146621620929431>). The bring.bilog() and bring.parscale() functions were written by modifying the read.bilog() and read.parscale() functions, respectively (Weeks (2010) <doi:10.18637/jss.v035.i12>). The bisection() function was written by modifying the bisection() function (Howard (2017, ISBN:9780367657918)). The code of the inverse test characteristic curve scoring in the est_score() function was written by modifying the irt.eq.tse() function (González (2014) <doi:10.18637/jss.v059.i07>). In est_score() function, the code of weighted likelihood estimation method was written by referring to the Pi(), Ji(), and Ii() functions of the catR package (Magis & Barrada (2017) <doi:10.18637/jss.v076.c01>).

Maintained by Hwanggyu Lim. Last updated 7 months ago.

2.3 match 2 stars 2.00 score 2 scripts

david-ti

WrightMap:IRT Item-Person Map with 'ConQuest' Integration

A powerful yet simple graphical tool available in the field of psychometrics is the Wright Map (also known as item maps or item-person maps), which presents the location of both respondents and items on the same scale. Wright Maps are commonly used to present the results of dichotomous or polytomous item response models. The 'WrightMap' package provides functions to create these plots from item parameters and person estimates stored as R objects. Although the package can be used in conjunction with any software used to estimate the IRT model (e.g. 'TAM', 'mirt', 'eRm' or 'IRToys' in 'R', or 'Stata', 'Mplus', etc.), 'WrightMap' features special integration with 'ConQuest' to facilitate reading and plotting its output directly.The 'wrightMap' function creates Wright Maps based on person estimates and item parameters produced by an item response analysis. The 'CQmodel' function reads output files created using 'ConQuest' software and creates a set of data frames for easy data manipulation, bundled in a 'CQmodel' object. The 'wrightMap' function can take a 'CQmodel' object as input or it can be used to create Wright Maps directly from data frames of person and item parameters.

Maintained by David Torres Irribarra. Last updated 5 months ago.

0.5 match 6 stars 6.58 score 65 scripts 3 dependents

cran

RMX:Rasch Models -- eXtensions

Extend Rasch and Item Response Theory (IRT) analyses by providing tools for post-processing the output from five major IRT packages (i.e., 'eRm', 'psychotools', 'ltm', 'mirt', and 'TAM'). The current version provides the plotPIccc() function, which extracts from the return object of the originating package all information required to draw an extended Person-Item-Map (PIccc), showing any combination of * category characteristic curves (CCCs), * threshold characteristic curves (TCCs), * item characteristic curves (ICCs), * category information functions (CIFs), * item information functions (IIFs), * test information function (TIF), and the * standard error curve (S.E.). for uni- and multidimensional models (as far as supported by each package). It allows for selecting dimensions, items, and categories to plot and offers numerous options to adapt the output. The return object contains all calculated values for further processing.

Maintained by Rainer W. Alexandrowicz. Last updated 6 months ago.

0.5 match 1.00 score

: